Abstract
This paper proposes a Full Range Gaussian Markov Random Field (FRGMRF) model for monochrome image compression, where images are assumed to be Gaussian Markov Random Field. The parameters of the model are estimated based on Bayesian approach. The advantage of the proposed model is that it adapts itself according to the nature of the data (image) because it has infinite structure with a finite number of parameters, and so completely avoids the problem of order determination. The proposed model is fitted to reconstruct the image with the use of estimated parameters and seed values. The residual image is computed from the original and the reconstructed images. The proposed FRGMRF model is redefined as an error model to compress the residual image to obtain better quality of the reconstructed image. The parameters of the error model are estimated by employing the Metropolis-Hastings (M-H) algorithm. Then, the error model is fitted to reconstruct the compressed residual image. The Arithmetic coding is employed on seed values, average of the residuals and the model coefficients of both the input and residual images to achieve higher compression ratio. Different types of textured and structured images are considered for experiment to illustrate the efficiency of the proposed model. The results obtained by the FRGMRF model are compared to the JPEG2000. The proposed approach yields higher compression ratio than the JPEG whereas it produces Peak Signal to Noise Ratio (PSNR) with little higher than the JPEG, which is negligible.
Highlights
Image content analysis is an important research issue in computer vision because applications such as multimedia, image retrieval through Internet, TV broadcast, and storage management etc. require high speed transmission of data and higher compression ratio with high quality
This paper proposes a Full Range Gaussian Markov Random Field (FRGMRF) model for monochrome image compression, where images are assumed to be Gaussian Markov Random Field
The procedure used for the actual input image (FRGMRF model) is adopted for the residual image
Summary
Image content analysis is an important research issue in computer vision because applications such as multimedia, image retrieval through Internet, TV broadcast, and storage management etc. require high speed transmission of data and higher compression ratio with high quality. Memon and Wu [5] proposed a scheme, CALIC, which is presented as a competitor for the standard; it gives similar or better performance at the increased computational complexity when compared to that of LOCO-I These schemes are based on the same idea of predicting a pixel value on the basis of the values of adjacent pixels. Though many researchers [9,10,11] have developed near-lossless compression algorithm based on differential pulse code modulation (DPCM) for continuous-tone images, there is a lack of decreasing the computational time as well as increasing the compression ratio. The proposed technique uses the predictive coding based statistical approach In this approach, after estimating the parameters, the model generates the pixel values within negligible time period.
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